Neural Network Approach for Nonlinear Aeroelastic Analysis
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Bibliographic record
Abstract
A new approach is proposed, based on the use of artificial neural networks, for predicting nonlinear aeroelastic oscillations. Our objective is to reconstruct the asymptotic state of the nonlinear behavior of an aeroelastic model when only a limited segment of the transient data is known. An original neural network architecture is proposed and is used to predict the nonlinear motions of an aeroelastic system modeling a self-excited two-degree-of-freedom airfoil oscillating in pitch and plunge. When a segment of the transient state of the given signal is used for training, the neural network is capable of correctly predicting the corresponding limit-cycle oscillations, damped oscillations, or unstable divergent oscillations. The network training set consists of numerically generated data or data obtained from a wind-tunnel experiment. A neural network used in conjunction with a wavelet decomposition is presented, which proves to be capable of extracting the values of the damping coefficients and frequencies from the predicted signal. Neural networks, thus, prove to be useful tools in nonlinear aeroelastic analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it